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Thinking Big презентация
Содержание
- 1. Thinking Big
- 2. About Me Shawn Hermans Data Engineer/Scientist Technology consultant Physics, math, data geek
- 3. About this Talk Non-technical introduction to Big
- 4. Should you believe the hype?
- 5. No need for scientific method Predict disease
- 7. Big Data Criticism Garbage in, Garbage
- 8. Big Data is just another way to think about data
- 9. Mental Models “A mental model is simply
- 10. Examples Occam's razor Mind maps Law of
- 11. All models are wrong, but some are useful
- 12. Relational Resistance Resistance to big data concepts,
- 14. Data Mental Models Relational Linked Object Oriented
- 15. What is Big Data?
- 16. “Big data is high volume, high velocity,
- 17. According to Me Big data is the Bazaar to traditional data’s Cathedral
- 18. Cathedral and Bazaar Traditional Data Clean Top
- 19. Big Data Differences Relational Normalization ACID SQL/Query Structured/Schema Big Data Denormalization BASE MapReduce/Other Loosely Structured
- 20. Integrating all available data is the promise of Big Data
- 21. Why should you care?
- 23. Information as an Asset Target specific customer's
- 24. Big Data and You What information do
- 25. Big Data Technology
- 26. Big Data Platforms Cloud AWS Google Microsoft
- 27. Big Data Stack Batch Processing Data Collection
- 29. What about data science?
- 30. Data science is statistics on a Mac
- 32. The need for Data Science There is
- 33. Big Data has its limits
- 34. Black Swans and Big Data There are
- 35. What’s next?
- 36. Business Identify some unresolved questions Figure out
- 37. My Info Twitter: @shawnhermans Github: github.com/shawnhermans Blog: http://shawnhermans.github.io/ (In Progress) Slideshare: www.slideshare.net/shawnhermans/ Quora: http://www.quora.com/Shawn-Hermans
- 38. Backup Slides
- 40. The Fourth Quadrant and the Failure of Statistics
- 41. Soothsayer Simple HTTP/JSON API for training/classifying
About Me Shawn Hermans Data Engineer/Scientist Technology consultant Physics, math, data geek
Слайд 3About this Talk
Non-technical introduction to Big Data
Not focused on any technology
or platform
Focus on concepts
Focus on concepts
Слайд 5No need for scientific method
Predict disease outbreaks before the CDC
Cure cancer
Innovating
healthcare
Solve world hunger
Bring about world peace
Solve world hunger
Bring about world peace
Big Data Promises
Слайд 7Big Data Criticism
Garbage in, Garbage out
Ignores the role of the
scientific method
Lots of questions don’t require large amounts of data to get good stats
Privacy issues
Lots of questions don’t require large amounts of data to get good stats
Privacy issues
Слайд 9Mental Models
“A mental model is simply a representation of an external
reality inside your head. Mental models are concerned with understanding knowledge about the world.”
- Farnam Street Blog
- Farnam Street Blog
Слайд 12Relational Resistance
Resistance to big data concepts, technologies, and techniques because of
belief that the relational model is the only way to think about data.
See also: Theory induced blindness
See also: Theory induced blindness
Слайд 14Data Mental Models
Relational
Linked
Object Oriented
Geospatial
Temporal
Semantic
Event Based
Data as Code
Bayesian
Unstructured
Слайд 16“Big data is high volume, high velocity, and/or high variety information
assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization.”
According to Gartner
Слайд 18Cathedral and Bazaar
Traditional Data
Clean
Top down
Carefully collected
Scales vertically
One true way
Big Data
Disorderly
Bottom up
Randomly
collected
Scales horizontally
More than one way
Scales horizontally
More than one way
Слайд 19Big Data Differences
Relational
Normalization
ACID
SQL/Query
Structured/Schema
Big Data
Denormalization
BASE
MapReduce/Other
Loosely Structured
Слайд 23Information as an Asset
Target specific customer's needs rather than broad segments
Just-in-time
inventory management
Evaluating demand for product
Predict and track traffic patterns
Evaluating demand for product
Predict and track traffic patterns
Слайд 24Big Data and You
What information do you have, that no one
else has?
Can you easily integrate your data or is it locked in silos?
What data don’t you collect?
What data don’t you archive?
Can you easily integrate your data or is it locked in silos?
What data don’t you collect?
What data don’t you archive?
Слайд 26Big Data Platforms
Cloud
AWS
Google
Microsoft
Hadoop
Cloudera
MapR
Hortonworks
This isn’t an all inclusive list, but a sample
of the big players in the space.
Слайд 27Big Data Stack
Batch Processing
Data Collection
SQL/Query
Search
Machine Learning
Serialization
Security
Stream Processing
File Storage
Resource management
Online NoSQL
Data Pipeline
Слайд 30Data science is statistics on a Mac
A data scientist is a
statistician who lives in San Francisco
Person who is better at statistics than any software engineer and better at software engineering than any statistician.
Person who is better at statistics than any software engineer and better at software engineering than any statistician.
What IS Data Science?
Слайд 32The need for Data Science
There is a LOT of data
Too much
data for people to look at it all
Probabilistic models help extract signal from the noise
Need to automate the analysis and exploitation of data
Probabilistic models help extract signal from the noise
Need to automate the analysis and exploitation of data
Слайд 34Black Swans and Big Data
There are fundamental limits to prediction
Hard to
predict rare events where no prior data exists (i.e. Black Swans)
Complex systems often have feedback loops (e.g. stock market)
Complex systems often have feedback loops (e.g. stock market)
Слайд 36Business
Identify some unresolved questions
Figure out what data could answer those questions
Pick
the easiest and test out your hypothesis
Getting Started
Technology
Pick a technology you know or want to learn
Pick a platform
Pick a data set and identify some basic problems to solve
Слайд 37My Info
Twitter: @shawnhermans
Github: github.com/shawnhermans
Blog: http://shawnhermans.github.io/ (In Progress)
Slideshare: www.slideshare.net/shawnhermans/
Quora: http://www.quora.com/Shawn-Hermans
Слайд 41Soothsayer
Simple HTTP/JSON API for training/classifying data
Lots of built in
classifier statistics
https://github.com/shawnhermans/soothsayer
